SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

Showing 96019625 of 10307 papers

TitleStatusHype
Cell Selection with Deep Reinforcement Learning in Sparse Mobile Crowdsensing0
CoNet: Collaborative Cross Networks for Cross-Domain RecommendationCode0
Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application0
NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer LearningCode0
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis0
Deep transfer learning in the assessment of the quality of protein modelsCode0
Network Signatures from Image Representation of Adjacency Matrices: Deep/Transfer Learning for Subgraph Classification0
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving0
Deep Embedding Kernel0
Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation TaskCode0
RIPEx: Extracting malicious IP addresses from security forums using cross-forum learning0
A Large-scale Attribute Dataset for Zero-shot LearningCode0
Plaque Classification in Coronary Arteries from IVOCT Images Using Convolutional Neural Networks and Transfer Learning0
Personalized Dynamics Models for Adaptive Assistive Navigation Systems0
Universal Successor Representations for Transfer Reinforcement Learning0
Gotta Learn Fast: A New Benchmark for Generalization in RLCode0
AMNet: Memorability Estimation with AttentionCode0
Assessment of Breast Cancer Histology using Densely Connected Convolutional Networks0
Markerless tracking of user-defined features with deep learning0
Towards Deep Cellular Phenotyping in Placental HistologyCode0
Impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning0
Bringing Cartoons to Life: Towards Improved Cartoon Face Detection and Recognition SystemsCode0
Boosting Handwriting Text Recognition in Small Databases with Transfer LearningCode0
Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars0
StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2Unverified
2DFA-ENTAccuracy69.2Unverified
3DFA-SAFNAccuracy69.1Unverified
4EasyTLAccuracy63.3Unverified
5MEDAAccuracy60.3Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95Unverified